Transcriptome sequencing of human hepatocellular carcinoma
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ABSTRACT: Deep high-throughput transcriptome sequencing (RNA-seq) performed on 3 pairs of matched tumor and adjacent non-tumorours (NT) tissues from HCC patients of Chinese origin generated 183.6-million reads that could be aligned. We discovered a number of differentially expressed genes and multiple types of somatic single nucleotide variations (SNVs) in expressed genes. After the removal of the error alignments, high-quality reads were mapped to the human reference sequence (GRCh37/hg19) using three different softwares TopHat, Burrows-Wheeler Aligner (BWA) and CLC Genomics Workbench (CLC). The high-quality variants were identified using VarScan with the following parameters: minimum coverage depth of 10, variation frequency of more than 30% and base quality of more than 15. A total of 568, 545 and 494 potential somatic single nucleotide variants (SNVs), including 94, 89 and 101 coding somatic SNVs (cSNVs), were identified in 3 tumor samples HCC448T, HCC473T and HCC510T, respectively. Validation analysis was carried out for 10 of the intersected cSNVs (all are non-synonymous substitutions) within selected genes of interests with the majority confirmed. Examination of 3 paired human hepatocellular carcinoma and matched non-tumor tissues
Project description:Deep high-throughput transcriptome sequencing (RNA-seq) performed on 3 pairs of matched tumor and adjacent non-tumorours (NT) tissues from HCC patients of Chinese origin generated 183.6-million reads that could be aligned. We discovered a number of differentially expressed genes and multiple types of somatic single nucleotide variations (SNVs) in expressed genes. After the removal of the error alignments, high-quality reads were mapped to the human reference sequence (GRCh37/hg19) using three different softwares TopHat, Burrows-Wheeler Aligner (BWA) and CLC Genomics Workbench (CLC). The high-quality variants were identified using VarScan with the following parameters: minimum coverage depth of 10, variation frequency of more than 30% and base quality of more than 15. A total of 568, 545 and 494 potential somatic single nucleotide variants (SNVs), including 94, 89 and 101 coding somatic SNVs (cSNVs), were identified in 3 tumor samples HCC448T, HCC473T and HCC510T, respectively. Validation analysis was carried out for 10 of the intersected cSNVs (all are non-synonymous substitutions) within selected genes of interests with the majority confirmed. Examination of 3 paired human hepatocellular carcinoma and matched non-tumor tissues
Project description:Deep high-throughput transcriptome sequencing (RNA-seq) performed on 3 pairs of matched tumor and adjacent non-tumorours (NT) tissues from HCC patients of Chinese origin generated 183.6-million reads that could be aligned. We discovered a number of differentially expressed genes and multiple types of somatic single nucleotide variations (SNVs) in expressed genes. After the removal of the error alignments, high-quality reads were mapped to the human reference sequence (GRCh37/hg19) using three different softwares TopHat, Burrows-Wheeler Aligner (BWA) and CLC Genomics Workbench (CLC). The high-quality variants were identified using VarScan with the following parameters: minimum coverage depth of 10, variation frequency of more than 30% and base quality of more than 15. A total of 568, 545 and 494 potential somatic single nucleotide variants (SNVs), including 94, 89 and 101 coding somatic SNVs (cSNVs), were identified in 3 tumor samples HCC448T, HCC473T and HCC510T, respectively. Validation analysis was carried out for 10 of the intersected cSNVs (all are non-synonymous substitutions) within selected genes of interests with the majority confirmed.
Project description:Transcriptome profiling of hepatocellular carcinoma (HCC) by next-generation sequencing (NGS) technology has been broadly performed by previous studies, which facilitate our understanding of the molecular mechanisms of HCC formation, progression, and metastasis. However, few studies jointly analyze multiple types of noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and micro-RNAs (miRNAs), and further uncover their implications in HCC. In this study, we observed that the circRNA cZRANB1 and lncRNA DUXAP10 were not only significantly upregulated in tumor tissues, but also higher expressed in blood exosomes of HCC as compared with healthy donors. From the analysis of subclass-associated dysregulated ncRNAs, we observed that DLX6-AS1, an antisense RNA of DLX6, and the sense gene DLX6 were highly expressed in S1, a subclass with a more invasive/disseminative phenotype. High correlation between DLX6-AS1 and DLX6 suggested that DLX6-AS1 may function via promoting the transcription of DLX6. Integrative analysis uncovers circRNA-miRNA, lncRNA-miRNA, and competing endogenous RNA networks (ceRNAs). Specifically, cZRANB1, LINC00501, CTD-2008L17.2, and SLC7A11-AS1 may function as ceRNAs that regulate mRNAs by competing the shared miRNAs. Further prognostic analysis demonstrated that the dysregulated ncRNAs had the potential to predict HCC patients' overall survival. In summary, we identified some novel circRNAs and miRNAs, and dysregulated ncRNAs that could participate in HCC tumorigenesis and progression by inducing transcription of their neighboring genes, increasing their derived miRNAs, or acting as miRNA sponges. Moreover, our systematic analysis provides not only rich data resources for related researchers, but also new insights into the molecular basis of how different ncRNAs coordinately or antagonistically participate in the pathogenesis process of diseases.
Project description:Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to develop genomic-based classification for HCC have yielded highly divergent results, indicating difficulty in identifying unified molecular anatomy. We performed a meta-analysis of gene expression profiles in data sets from eight independent patient cohorts across the world. In addition, aiming to establish the real world applicability of a classification system, we profiled 118 formalin-fixed, paraffin-embedded tissues from an additional patient cohort. A total of 603 patients were analyzed, representing the major etiologies of HCC (hepatitis B and C) collected from Western and Eastern countries. We observed three robust HCC subclasses (termed S1, S2, and S3), each correlated with clinical parameters such as tumor size, extent of cellular differentiation, and serum alpha-fetoprotein levels. An analysis of the components of the signatures indicated that S1 reflected aberrant activation of the WNT signaling pathway, S2 was characterized by proliferation as well as MYC and AKT activation, and S3 was associated with hepatocyte differentiation. Functional studies indicated that the WNT pathway activation signature characteristic of S1 tumors was not simply the result of beta-catenin mutation but rather was the result of transforming growth factor-beta activation, thus representing a new mechanism of WNT pathway activation in HCC. These experiments establish the first consensus classification framework for HCC based on gene expression profiles and highlight the power of integrating multiple data sets to define a robust molecular taxonomy of the disease.
Project description:The long reads of Nanopore sequencing permit accurate transcript assembly and ease in discovering novel transcripts with potentially important functions in cancers. The wide adoption of Nanopore sequencing for transcript quantification, however, is largely limited by high costs. To address this issue, we developed a bioinformatics software, NovelQuant, that can specifically quantify long-read-assembled novel transcripts with short-read sequencing data. Nanopore Direct RNA Sequencing was carried out on three hepatocellular carcinoma (HCC) patients' tumor, matched portal vein tumor thrombus, and peritumor to reconstruct the HCC transcriptome. Then, based on the reconstructed transcriptome, NovelQuant was applied on Illumina RNA sequencing data of 59 HCC patients' tumor and paired peritumor to quantify novel transcripts. Our further analysis revealed 361 novel transcripts dysregulated in HCC and that 101 of them were significantly associated with prognosis. There were 19 novel prognostic transcripts predicted to be long noncoding RNAs (lncRNAs), and some of them had regulatory targets that were reported to be associated with HCC. Additionally, 42 novel prognostic transcripts were predicted to be protein-coding mRNAs, and many of them could be involved in xenobiotic metabolism. Moreover, the tumor-suppressive roles of two representative novel prognostic transcripts, CDO1-novel (lncRNA) and CYP2A6-novel (protein-coding mRNA), were further functionally validated during HCC progression. Overall, the current study shows a possibility of combining long- and short-read sequencing to explore functionally important novel transcripts in HCC with accuracy and cost-efficiency, which expands the pool of molecular biomarkers that could enhance our understanding of the molecular mechanisms of HCC.
Project description:Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death. The aim of this study was to identify underlying hub genes and dysregulated pathways associated with the development of HCC using bioinformatics analysis. Differentially expressed protein-coding genes were subjected to transcriptome sequencing in 11 pairs of liver cancer tissue and matched adjacent non-cancerous tissue. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by protein-protein interaction (PPI) network construction. Hub genes were identified via centralities analysis and verified using published datasets. In total, 720 significantly differentially expressed protein-coding genes were identified in the samples, including 335 upregulated genes and 385 downregulated genes. The upregulated genes were significantly enriched in cell adhesion, biological adhesion and cell-cell adhesion GO terms under biological process (BP). Conversely, the downregulated genes were significantly enriched in embryonic organ morphogenesis, embryonic organ development and embryonic morphogenesis. The KEGG pathway analysis showed that the upregulated genes were enriched in ECM-receptor interaction and focal adhesion pathways. Furthermore, the downregulated genes were enriched in the ErbB, VEGF and MAPK signaling pathways. The PPI network and centralities analysis suggested that ITGA2 and 12 alternate genes were significant hub genes. These findings improve current understanding of the molecular mechanisms underlying HCC development and may be helpful in identifying candidate molecular biomarkers for use in diagnosing, treating and monitoring the prognosis of HCC.
Project description:Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and it remains a challenge to understand the genetic mechanisms underlying hepatocarcinogenesis. A global gene network of differential expression profiles in HCC has yet to be fully characterized. In the present study, we performed transcriptome sequencing (mRNA and lncRNA) in liver cancer and cirrhotic tissues of nine HCC patients. We identified differentially expressed genes (DEGs) and constructed a weighted gene co-expression network for the DEGs. In total, 755 DEGs (747 mRNA and eight lncRNA) were identified, and several co-expression modules were significantly associated with HCC clinical traits, including tumor location, tumor grade, and the α-fetoprotein (AFP) level. Of note, we identified 15 hub genes in the module associated with AFP level, and three (SPX, AFP and ADGRE1) of four hub genes were validated in an independent HCC cohort (n=78). Identification of hub genes for HCC clinical traits has implications for further understanding of the molecular genetic basis of HCC.
Project description:MicroRNAs (miRNAs) participate in crucial biological processes, and it is now evident that miRNA alterations are involved in the progression of human cancers. Recent studies on miRNA profiling performed with cloning suggest that sequencing is useful for the detection of novel miRNAs, modifications, and precise compositions and that miRNA expression levels calculated by clone count are reproducible. Here we focus on sequencing of miRNA to obtain a comprehensive profile and characterization of these transcriptomes as they relate to human liver. Sequencing using 454 sequencing and conventional cloning from 22 pair of HCC and adjacent normal liver (ANL) and 3 HCC cell lines identified reliable reads of more than 314000 miRNAs from HCC and more than 268000 from ANL for registered human miRNAs. Computational bioinformatics identified 7 novel miRNAs with high conservation, 15 novel opposite miRNAs, and 3 novel antisense miRNAs. Moreover sequencing can detect miRNA modifications including adenosine-to-inosine editing in miR-376 families. Expression profiling using clone count analysis was used to identify miRNAs that are expressed aberrantly in liver cancer including miR-122, miR-21, and miR-34a. Furthermore, sequencing-based miRNA clustering, but not individual miRNA, detects high risk patients who have high potentials for early tumor recurrence after liver surgery (P = 0.006), and which is the only significant variable among pathological and clinical and variables (P = 0,022). We believe that the combination of sequencing and bioinformatics will accelerate the discovery of novel miRNAs and biomarkers involved in human liver cancer.